DocumentCode :
3390560
Title :
An effective denial of service detection method using kernel based data
Author :
Chung, Manhyun ; Cho, Jaeik ; Moon, Jongsub
Author_Institution :
Center for Inf. Security Technol., Korea Univ., Seoul
fYear :
2009
fDate :
March 30 2009-April 2 2009
Firstpage :
9
Lastpage :
12
Abstract :
Today much research is being done on host based intrusion detection systems using of kernel based data. However, kernel based data also known as system calls, have a vast variety, which leads to large amounts of preprocessing time when implementing to intrusion detection systems. This paper proposes a method to efficiently detect denial of service attacks, which are continuous threat. Principal Component Analysis will be used to derive the principal components, a Bayesian network will be composed and the Bayesian classifier will be used for the detection.
Keywords :
belief networks; principal component analysis; security of data; Bayesian classifier; denial of service detection method; intrusion detection systems; kernel based data; principal component analysis; Bayesian methods; Classification algorithms; Computer crime; Data mining; Frequency; Information security; Intrusion detection; Kernel; Moon; Principal component analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence in Cyber Security, 2009. CICS '09. IEEE Symposium on
Conference_Location :
Nashville, TN
Print_ISBN :
978-1-4244-2769-7
Type :
conf
DOI :
10.1109/CICYBS.2009.4925083
Filename :
4925083
Link To Document :
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